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Principal Software Engineer

Morningstar
Toronto, ON

Postuler dès maintenant

Détails du poste

Temps plein
Il y a 4 jours

Compétences

Azure
Kubernetes
Spark
Microsoft SQL Server
Google Cloud Platform
SQL
AWS
Docker
PostgreSQL
Systèmes distribués
GitHub
S3
Apache
Intelligence artificielle
Python

Description complète du poste

The Role

As a Principal Software Engineer on our Data Feed Platform team (Direct – Data & Research), you will partner with product owners and engineering teams to shape the technical direction of our data engineering capability. Together, you will migrate our file-based products to a unified, cloud-native data platform — architecting highly governed data pipelines, feed generation systems, and large-scale data delivery infrastructure.

This is a senior individual contributor role reporting to the Director of Technology. You will serve as a technical thought leader for a team of engineers — owning the end-to-end data platform architecture, from ingestion and transformation through to client-facing data products. You will define best practices for data governance, data modeling, performance optimization, and data reliability across the entire product lifecycle, while mentoring engineers and fostering continuous improvement within the data engineering discipline.

If your background is in data engineering, data platform development, or building production-grade data systems at scale, this role was designed for you.

Location: Toronto, ON (Hybrid-4 days in Office)

We intentionally prioritize in-person collaboration, as we've found it strengthens creative quality, alignment, and team momentum.

Job Responsibilities

Lead and provide deep technical direction across data feeds and the data engineering function, guiding architectural decisions across platforms.
Architect the platform consolidation strategy, migrating legacy feed products onto a unified, governed, cloud-native architecture.
Design and implement scalable data delivery mechanisms for both file-based feeds and modern marketplace distribution platforms.
Drive DataOps maturity by establishing comprehensive data quality, monitoring, alerting, and CI/CD practices across the platform.
Influence technical strategy across teams by communicating architectural vision to both technical and non-technical stakeholders.

Qualifications

Experience: 9+ years of experience in data engineering, data platforms, or distributed systems.
Cloud Expertise: Proven track record building and optimizing large-scale data pipelines on a major cloud platform (AWS preferred; Azure or GCP also accepted).
Data Processing at Scale: Strong experience with distributed or high-performance compute engines for large-scale data transformation. Familiarity with frameworks such as Spark/PySpark , DuckDB , or similar modern engines, and the ability to evaluate trade-offs between them for different workloads.
SQL & Programming: Expert proficiency in SQL (Postgres, SQL Server, etc) and strong development skills in Python (Python 3.x).
Data Warehousing: Strong hands-on experience with modern cloud data warehouses (e.g., Snowflake, Databricks, Redshift).
Technical Leadership: Demonstrated ability to influence engineering direction without direct management authority, mentor engineers, and drive alignment across teams.
Deployment: Experience with containerization (Docker, Kubernetes).
Cloud Storage: Hands-on experience with cloud object storage (AWS S3, Azure Blob Storage, or Google Cloud Storage).

Nice to Have (Experience & Tools)

Architecture: Knowledge of data lake and lakehouse architecture, including the implementation and use of open table formats like Delta Lake and Apache Iceberg .
Domain Knowledge: Previous experience in highly regulated or financial services industries with stringent data quality and delivery SLA requirements.
AI-Assisted Development: Experience using agentic coding tools (e.g., GitHub Copilot, Claude Code, Cursor) to accelerate development workflows.

Base Salary Compensation Range

$112,583.00-162,125.00

Incentive Target Percentage

20% Annual

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.

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